Primary ovarian insufficiency (POI) is part of the continuum of ovarian dysfunction ranging from infertility with a high FSH level to early menopause before age 45 years, and affects 5-10% of women. While it has been suggested in epidemiology studies that women with POI suffer from cardiovascular disease, osteoporosis and increased mortality, the breadth of comorbid disease and the relationship between POI and mortality is not clear. Further, the genetic underpinnings of POI may predispose women to diseases that have not been defined and may overlap with genes affecting male fertility. We will examine the familial segregation of POI, male infertility, comorbid disease and mortality to uncover these relationships. We will then apply unique DNA sequence analysis software developed at the University of Utah to discover novel gene mutations in familial POI cases and determine whether they also segregate with comorbid disease. Specific Aim 1 identifies familial cases of POI in the Utah Population Database, the most extensive genealogical database in the U.S. The inheritance pattern, family size and intersection with male infertility will be examined. Specific Aim 2 examines associated phenotypes, comorbid diseases and mortality in familial POI to determine its effect on overall health and to determine families at risk for POI. Specific Aim 3 will identify genetic mutations in women with familial POI through whole exome or whole genome sequencing and determine whether these mutations also segregate with comorbid disease. We will use novel software (pVAAST and Phevor) developed at the University of Utah and controls recruited for health in old age to prioritize variants in familial POI. The software identifies rare, damaging variants in genes that have a strong relationship to the POI phenotype. Variants will be replicated using three cohorts of women with sporadic POI (n=269). The work addresses fertility as a marker of overall health by identifying diseases associated with decreased ovarian reserve and POI. It also illuminates our understanding of the genetics of the reproductive aging transition. Using results from this proposal, we will be able to determine disease risk in women with POI. Conversely, we will use family history and associated disorders to identify women at risk for POI. New gene mutations and pathways will inform software algorithms such as Phevor, which will use the new information to prioritize variants discovered in next generation sequencing. The algorithms may eventually be used to predict fertility after whole genome sequencing or determine the genetic cause of POI in additional women. Early identification will bring the potential to preserve fertility and create targeted treatment options for these women. Conversely, assessing comorbid disease risk in women with POI will target patients for disease prevention after infertility treatment.